Digital Transformation

The Democratization of Data – It’s Like Riding a Bike

Since the original Data Warehouse, Business Intelligence and Executive Information Systems came into being, the ability to look at data and make meaningful decisions with confidence has been the conserve of those who either are sufficiently senior to coral the teams and resources necessary OR those who already understand the data and its meaning in the sources from which it comes.

However, with the explosion of Big Data (a combination of new infrastructure, data management methods and intelligent tooling) has arrived the promise of business user driven access to data and information is becoming a reality.

At first glance, this is highly attractive (for today’s knowledge workers, the availability of real, accurate, timely data presented as easily consumed information is a Holy Grail), but with this attractiveness comes high expectations, from which it is all to easy to under-deliver. Big Data driven democratization CAN deliver more flexible analysis, more frequently, and with reduced time needed on data manipulation and preparation. However, delivery teams should not be tempted to rush the roll out of ‘all encompassing data lakes and the latest visual analysis tooling’.


In order to reduce the risk of under delivery, I resort to my children’s attempts to learn to ride. The expectations are high having witnessed Team GB’s success, speeding through the field with apparent ease…their expectations were similarly high. However, once furnished with their new bike, on the first attempt, a crash is the result and the inevitable reluctance to try again, blaming the bike…the answer is of course, a combination of short runs, on soft grass, with training wheels and an adult running along behind.

The same is required when embarking on a Big Data lead initiative. These support factors come in the shape of the following:

  1. A data governance function which delivers data quality, metadata management, lineage and business glossary alongside the data itself – this gives confidence in experimentation.
  2. A small data subset, and a small group of new tooling, tailored for specific users levels of sophistication (and time available!) – this is the short run and soft landing.
  3. Sufficient guidance providing support from the Big Data delivery team appropriately versed in the business use cases, and the ability to listen to what is being attempted and provide proactive changes to achieve the result or suggest new ways of working with the data – the grown up running along behind.

Once the users have the initial confidence, support can gradually be removed, and focused on the more demanding aspects which will inevitably arise – advanced visualisation, cognitive analytics and into streaming analytics. Or in my analogy, removal of training wheels and looking forward to countryside bike rides sampling the local produce!

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